2009South African Statistical JournalOpen access

The central and non-central matrix variate Dirichlet type III distribution : theory and methods

René Ehlers, Andriëtte Bekker, J. J. J. Roux

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Abstract

The methods of multivariate analysis depend upon the multivariate normal distribution. If a sample is drawn from a multivariate normal distribution, the sample covariance matrix has a Wishart distribution. Therefore, this distribution plays an important role in inferential procedures. The matrix variate Beta distribution is closely connected to the Wishart distribution.

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The methods of multivariate analysis depend upon the multivariate normal distribution. If a sample is drawn from a multivariate normal distribution, the sample covariance matrix has a Wishart distribution. Therefore, this distribution plays an important role in inferential procedures. The matrix variate Beta distribution is closely connected to the Wishart distribution.

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Available abstract

The methods of multivariate analysis depend upon the multivariate normal distribution. If a sample is drawn from a multivariate normal distribution, the sample covariance matrix has a Wishart distribution. Therefore, this distribution plays an important role in inferential procedures. The matrix variate Beta distribution is closely connected to the Wishart distribution.

Key concepts: Wishart distribution, Matrix t-distribution, Matrix normal distribution, Inverse-Wishart distribution, Mathematics, Normal-Wishart distribution, Random variate, Scatter matrix

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